Jeffrey Theodore Heaton

Jeffrey Theodore Heaton
Washington University in St. Louis | WUSTL , Wash U · Sever Institute

PhD

About

47
Publications
120,290
Reads
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1,525
Citations
Introduction
Jeff Heaton, Ph.D., is a vice president and data scientist at Reinsurance Group of America (RGA), an adjunct instructor for the Sever Institute at Washington University, and the author of several books about artificial intelligence. Jeff holds a Master of Information Management (MIM) from Washington University and a Ph.D. in computer science from Nova Southeastern University.
Additional affiliations
August 2016 - present
Washington University in St. Louis
Position
  • Instructor
Description
  • My course is T81-558: Application of Deep Learning, it is a technical graduate course that covers deep learning using Python, Scikit-Learn, Pandas, and TensorFlow. I also occasionally talks/seminars for Washington University.
January 2004 - December 2006
Maryville University
Position
  • Instructor
Description
  • Taught classes on SQL, VB and C# programming.
January 1997 - December 2004
St. Louis Community College
Position
  • Instructor
Description
  • Designed and taught courses on Java, C/C++, UNIX Administration, C# and SQL.
Education
September 2014 - December 2018
Nova Southeastern University
Field of study
  • Computer Science
January 2002 - December 2005
Washington University in St. Louis
Field of study
  • Information Management
January 1996 - December 1998
Fontbonne College
Field of study
  • Business Administration

Publications

Publications (47)
Article
Full-text available
This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably wi...
Conference Paper
Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new features from the provided feature se...
Book
Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated an...
Book
Full-text available
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hier...
Article
Full-text available
We present MergeLife, a genetic algorithm (GA) capable of evolving continuous cellular automata (CA) that generate full color dynamic animations according to aesthetic user specifications. A simple 16-byte update rule is introduced that is evolved through an objective function that requires only initial human aesthetic guidelines. This update rule...
Conference Paper
Network intrusion detection systems are widely deployed to detect cyberattacks against computer networks. These systems generate large numbers of security alerts that require manual review by security analysts to determine the appropriate courses of action required. The review of these security alerts is time consuming and can cause fatigue for sec...
Conference Paper
Feature importance is the process where the individual elements of a machine learning model's feature vector are ranked on their relative importance to the accuracy of that model. Some feature ranking algorithms are specific to a single model type, such as Garson and Goh's neural network weight-based feature ranking algorithm. Other feature ranking...
Thesis
Full-text available
Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model’s predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature...
Article
Full-text available
Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While...
Article
Machine learning models, such as neural networks, decision trees, random forests and gradient boosting machines accept a feature vector and provide a prediction. These models learn in a supervised fashion where a set of feature vectors with expected output is provided. It is very common practice to engineer new features from the provided feature se...
Presentation
Full-text available
Like many other areas, predictive modeling is finding great application in the field of Information Assurance (IA). This presentation will present the latest advances in data science and their application to security. The presenter will compare and contrast a variety of current technologies, such as deep learning, general-purpose GPU (GPGPU), long...
Conference Paper
Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While...
Technical Report
Full-text available
During the 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA) a paper was presented that detailed a neural network-based intrusion detection system (IDS) that performed well on the KDD99 dataset. This paper also investigated several hidden layer topologies and attempted to determine the topology that provided the best root mean...
Book
Full-text available
Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering t...
Book
Full-text available
Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering...
Book
Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated anneal...

Network

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Projects

Projects (2)
Archived project
Create an augmented feature vector that will benefit a deep neural network. Genetic programming is used to search the space of available expressions.